io.hpp

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#ifndef PCL_KDTREE_IO_IMPL_HPP_
#define PCL_KDTREE_IO_IMPL_HPP_

#include <pcl/kdtree/io.h>
#include <pcl/kdtree/kdtree_flann.h>

template <typename Point1T, typename Point2T> void
pcl::getApproximateIndices (
    const typename pcl::PointCloud<Point1T>::Ptr &cloud_in, 
    const typename pcl::PointCloud<Point2T>::Ptr &cloud_ref, 
    std::vector<int> &indices)
{
  pcl::KdTreeFLANN<Point2T> tree;
  tree.setInputCloud (cloud_ref);

  std::vector<int> nn_idx (1);
  std::vector<float> nn_dists (1);
  indices.resize (cloud_in->points.size ());
  for (size_t i = 0; i < cloud_in->points.size (); ++i)
  {
    tree.nearestKSearch (*cloud_in, i, 1, nn_idx, nn_dists);
    indices[i] = nn_idx[0];
  }
}

template <typename PointT> void
pcl::getApproximateIndices (
    const typename pcl::PointCloud<PointT>::Ptr &cloud_in, 
    const typename pcl::PointCloud<PointT>::Ptr &cloud_ref, 
    std::vector<int> &indices)
{
  pcl::KdTreeFLANN<PointT> tree;
  tree.setInputCloud (cloud_ref);

  std::vector<int> nn_idx (1);
  std::vector<float> nn_dists (1);
  indices.resize (cloud_in->points.size ());
  for (size_t i = 0; i < cloud_in->points.size (); ++i)
  {
    tree.nearestKSearch (*cloud_in, i, 1, nn_idx, nn_dists);
    indices[i] = nn_idx[0];
  }
}

#endif // PCL_KDTREE_IO_IMPL_H_